Báo cáo hóa học: "Research Article Admission Control and Interference Management in Dynamic Spectrum Access Networks" pot

Hindawi Publishing Corporation
EURASIP Journal on Wireless Communications and Networking
Volume 2010, Article ID 708029, 11 pages
doi:10.1155/2010/708029
Research Article
Admission Control and Interference Management in
Dynamic Spectrum Access Networks
Jorge Martinez-Bauset, Vicent Pla, M. Jose Domenech-Benlloch, and Diego Pacheco-Paramo
Departamento de Comunicaciones, Universidad Polit
´
ecnica de Valencia (UPV), Camino de Vera s/n, 46022 Valencia, Spain
Correspondence should be addressed to Jorge Martinez-Bauset,
Received 6 October 2009; Revised 16 February 2010; Accepted 9 May 2010
Academic Editor: Gian Luigi Ferrari
Copyright © 2010 Jorge Martinez-Bauset et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
We study two important aspects to make dynamic spectrum access work in practice: the admission policy of secondary users (SUs)
to achieve a certain degree of quality of service and the management of the interference caused by SUs to primary users (PUs).
In order to limit the forced termination probability of SUs, we evaluate the Fractional Guard Channel reservation scheme to give
priority to spectrum handovers over new arrivals. We show that, contrary to what has been proposed, the throughput of SUs
cannot be maximized by configuring the reservation parameter. We also study the interference caused by SUs to PUs. We propose
and evaluate different mechanisms to reduce the interference, which are based on simple spectrum access algorithms for both PUs
and SUs and channel repacking algorithms for SUs. Numerical results show that the reduction can be of one order of magnitude
or more with respect to the random access case. Finally, we propose an adaptive admission control scheme that is able to limit
simultaneously the forced termination probability of SUs and what we define as the probability of interference. Our scheme does
not require any configuration parameters beyond the probability objectives. Besides, it is simple to implement and it can operate
with any arrival process and distribution of the session duration.
1. Introduction
Cognitive radio networks are envisaged as the key technology
to realize dynamic spectrum access (DSA). Such paradigm

shift in wireless communications aims at solving the scarcity
of radio spectrum [1–4]. The DSA concept proposes to
boost spectrum utilization by allowing DSA users (SUs)
to access the licensed wireless channel in an opportunistic
manner so that interference to licensed users (PUs) is kept
to a minimum. The idea of DSA is undoubtedly compelling
and its realization will induce a huge advance in wireless
communications. However, there are many challenges and
open questions that have to be addressed before DSA
networks become practically realizable [5, 6].
To fulfill the requirement of minimum interference to
PUs, a SU with an ongoing communication must vacate
the channel when a licensed user is detected. The SU may
switch to a different unused spectrum band which is referred
to as spectrum mobility or spectrum handover (SH). If no
available bands can be found or the SH procedure is not
implemented, one or more SUs will be forced to terminate
their sessions. From the user’s perspective, it is generally
assumed that the interruption of an ongoing session is more
annoying than denying initial access [7]. Therefore, blocking
the request of a new SU session, even if there are enough
free resources, can be employed as a strategy to reduce
the number of SU sessions forcedly terminated and the
interference caused to PUs.
A variety of studies that focus on priority mechanisms
to handle conventional handovers in cellular networks have
appeared in the literature, see [8] and references therein.
However, SH and conventional handover are different in
nature and also from a modeling perspective.
In this paper, we focus on the study of the Quality

of Service (QoS) perceived by PUs and SUs at the session
level. We employ the same rather simple model than [9],
which is enhanced to include an extension of the reservation
scheme so that a noninteger number of channels can be
reserved for SH. Such extension borrows the idea from
the Fractional Guard Channel scheme that was introduced
2 EURASIP Journal on Wireless Communications and Networking
in cellular networks [10]. Furthermore, our numerical
results for the system throughput are qualitatively different
from those obtained in [9] leading to completely different
conclusions, especially in what concerns the optimum system
configuration.
Interference management has been identified as one
of the critical challenges to make DSA networks work
in practice [6]. Common DSA proposals take a reactive
approach, in which SUs perform SH only after detecting
PU interference. To detect PU activity in the same band,
aSUmustperformspectrum sensing,whichrequiresto
pause any ongoing transmission and causes a considerable
performance penalty [6]. Additionally, SUs must execute
spectrum sensing frequently to react quickly when a PU
occupies the same band [11]. To handle both requirements,
transmission and spectrum sensing episodes are typically
interleaved in a cyclic manner [12, 13].
We study the interference management problem from the
traffic perspective. Our perception is that the mechanisms we
propose might have a complementary role with respect to
those defined at the physical layer. Our work is motivated by
the fact that although simple spectrum access and channel
repacking algorithms have been proposed in the classical

communications literature their application to DSA systems
has not been explored yet. In this paper, we assume that
the primary network follows a predefined deterministic
pattern when searching for free channels to set up a new
session. The secondary network is aware of the rule followed
by the primary network and uses this information in its
own benefit but also in that of the primary network. The
secondary network senses and assigns free channels to SUs
in the reversed order that they will be occupied by PUs,
hence reducing the probability of SUs having to vacate
the assigned channel and causing interference to PUs. The
probability of causing interference may be further reduced
by performing a channel rearrangement to SUs after the
release of channel. The mechanisms described above entail a
minimal cooperation of the primary network, which in turn
redound in a reduced interference for PUs. The idea of the
primary network cooperating with the secondary one has
also been proposed in [14].
We will show that both the forced termination probability
and the interference created by the operation of SUs upon
PUs can be controlled by limiting the access of SUs. This
finding motivated us to design an admission control scheme
for SUs that is able to limit simultaneously both the forced
termination probability of SUs and what we define as the
probability of interference. We show that both the forced
termination probability and the interference caused to PUs
are highly dependent on system parameters and on the
arrival processes and service distributions. However, the
proposed scheme is self-adaptive and does not require any
configuration parameters beyond the targeted QoS objec-

tives. Besides, it does not rely on any particular assumptions
on the traffic characteristics; that is, it can operate with any
arrival process and distribution of the session duration.
The rest of the paper is structured as follows. The
different models of the systems studied are described in
Section 2.InSection 3, we evaluate numerically the impact
of incorporating admission control on the forced termi-
nation of SUs and also the impact of deploying channel
allocation with preference and repacking on the interference.
In Section 4, we propose and evaluate a novel adaptive
admission control scheme that is able to limit simultaneously
both the forced termination probability and the interference.
Finally, Section 5 concludes the paper.
2. Model Description
We consider an infrastructure-based DSA network where
PUs and SUs cooperate. Infrastructure-based DSA networks
have been proposed in [2, 6, 15]. We assume that channels
available for system operation are numbered according to the
order in which they are assigned by the primary network; that
is, we consider that to setup a PU session, the system searches
from left (low-channel numbers) to right (high-channel
numbers) until enough free channels can be allocated to the
new session. Conversely, to setup a new SU communication
the system searches from right (high-channel numbers) to
left (low-channel numbers). We call this mechanism channel
allocation with preference (CAP). Additionally, once a PU or
a SU session has finished, a channel repacking of ongoing SU
sessions can be performed to avoid interfering with future
PU arrivals. Channel repacking can be triggered when, after
a session completion, there exist ongoing SU sessions that

can be moved to higher channel numbers; that is, there exist
ongoing SU sessions that can perform a preventive SH to
avoid creating future interference.
The system has a total of C resource units, being the
physical meaning of a unit of resource dependent on the spe-
cific technological implementation of the radio interface. For
the sake of mathematical tractability, we make the common
assumptions of Poisson arrival processes and exponentially
distributed service times. However, we also study the impact
that distributions different than the exponential for the
session lifetime have on system performance. The arrival rate
for PU (SU) sessions to the system is λ
1
(λ
2
), and a request
consumes b
1
(b
2
) resource units when accepted, b
i
∈ N,
i
= 1, 2. For a packet-based air interface, b
i
represents the
effective bandwidth of the session [16, 17]. We assume that
b
1

= N, b
2
= 1andC = M × N, therefore the system
resources can be viewed as composed by M
= C/N bands
for PUs or M
× N subbands or channels for SUs. In other
words, the maximum number of ongoing PU sessions is M
and of SU sessions is M
×N. The service rates for primary and
secondary sessions are denoted by μ
1
and μ
2
,respectively.
We s tudy seven d ifferent systems that can be aggregated
into three groups. The characteristics of each of the seven
systems are defined in Ta ble 1. The second (SH), third
(AC-FT) and sixth (AC-FT&I) columns refer, respectively,
to spectrum handoff mechanism, the admission control
(AC) scheme to limit the forced termination (FT) of SUs,
and the adaptive AC scheme that limits simultaneously
the forced termination probability perceived by SUs (P
ft
2
)
and the interference caused to PUs. On these columns, a
“Y” means that the systems implements the corresponding
mechanism and a “N” that it is not implemented. The
fourth (CA) and fifth (RP) columns refer, respectively, to the

EURASIP Journal on Wireless Communications and Networking 3
Table 1: Features of the systems studied.
System SH AC-FT CA RP AC-FT&I
S1 N N R N N
S2 Y Y R N N
S3a N N P N N
S3b Y N P N N
S4 Y N P Y N
S5a N Y P N Y
S5b Y Y P Y Y
channel allocation, which can be either random (“R”) or with
preference (“P”); and the the repacking mechanism, which is
either implemented (“Y”) or not (“N”).
In the following subsections, we introduce analytical and
simulation models to study the systems described in Ta bl e 1.
In Section 2.1 we present two continuous-time Markov chain
(CTMC) models that define the operation of systems 1 (S1)
and 2 (S2). The aim is to use these models to evaluate
the effectiveness of AC to limit P
ft
2
. Numerical results of
this evaluation are shown in Section 3.InSection 2.2,we
briefly outline two CTMC models that define the operation
of systems 3 (S3) and 4 (S4). The aim is to use these models to
compare the interference in a system deploying the proposed
CAP and repacking schemes with the interference in the
conventional random channel allocation scheme. Numerical
results of this evaluation are also shown in Section 3. Finally,
the model of the adaptive AC scheme deployed in system 5

(S5) and its evaluation is described in Section 4.
2.1. AC Scheme to Limit the Forced Termination of SUs. We
denote by x
= (x
1
, x
2
) the system state vector, when there
are x
1
ongoing PU sessions and x
2
SU sessions. Let b(x)
represent the amount of occupied resources at state x, b(x)
=
x
1
N + x
2
. The system evolution along time can be modeled
as a multidimensional Markov process whose set of feasible
states is
S :
={x =
(
x
1
, x
2
)

: x
1
N + x
2
≤ C}.
(1)
We develop two analytical models to evaluate the perfor-
mance of DSA systems measured by the forced termination
probability of SUs.
2.1.1. System 1. This first system is characterized by not
supporting SH, deploying the Complete Sharing admission
policy, that is, all SU requests are accepted while free
resources are available, deploying a random channel alloca-
tion scheme with no repacking.
APUarrivalinstatex will force the termination of k SUs,
k
= 0, , min(x
2
, N), with probability
p
(
x, k
)
=

N
k

(
M

−x
1
−1
)
N
x
2
−k


(
M
−x
1
)
N
x
2

. (2)
when k SUs are in the channels occupied by the newly arrived
PU session, while the other (x
2
− k) are distributed in the
other (M
−x
1
−1)N channels. Clearly,
min
(

x
2
,N
)

k=0
p
(
x, k
)
= 1.
(3)
Let r
xy
be the transition rate from x to y, x, y ∈ S,andbe
e
i
a vector whose entries are all 0 except the ith one, which is
1, then
r
xy
=
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪

⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
p
(
x, k
)
λ
1
if y = x + e
1
−ke
2
,
k
= 0, ,min

(
x
2
, N
)
,
λ
2
if y = x + e
2
,
x
i
μ
i
if y = x −e
i
, i = 1, 2,
0 otherwise.
(4)
Figure 1 shows the state diagram and transition rates of the
CTMC that models the system dynamics. The global balance
equations are expressed as
π
(
x
)

y∈S
r

xy
=

y∈S
π

y

r
yx
∀x ∈ S,
(5)
where π(x) is the stationary probabilityof state x.The
stationary distribution
{π(x)} is obtained from (5) and the
normalization equation.
The blocking probability for SU requests, P
2
, and the SUs
forced termination probability, P
ft
2
, can be determined from
the stationary distribution. Let us define
k
(
x
)
=
min

(
x
2
,N
)

r=0
rp
(
x, r
)
.
(6)
Clearly, k(x) is the mean number of SUs that are forced to
terminate upon the arrival of a PU in state x.Then,
P
2
=

x∈S,x+e
2
/
∈S
π
(
x
)
,
(7)
P

ft
2
=

x∈S
k
(
x
)
π
(
x
)
λ
1
λ
2
(
1
−P
2
)
.
(8)
Note that P
ft
2
is the ratio of the forced termination rate to the
acceptance rate.
Finally, the SUs throughput, that is, the successful

completion rate of SUs is determined by
Th
2
= λ
2
(
1
−P
2
)

1 −P
ft
2

. (9)
2.1.2. System 2. This system is characterized by supporting
SH, deploying the Fractional Guard Channel admission
policy and deploying the random channel allocation scheme
with no repacking.
4 EURASIP Journal on Wireless Communications and Networking
λ
1
λ
2
x
2
μ
2
x

1
μ
1
x + e
1
ke
2
x + e
2
x −
−
e
1
x − e
2
x
Figure 1: State transition rates of the CTMC from a generic state
x
∈ S.
When a SU new setup request arrives and finds the
system in state x, an admission decision is taken according
to the number of free resource units available:
C
−b
(
x + e
2
)
⎧
⎪

⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
> t accept,
=t reject with probability t −t,
<
t reject,
(10)
where we denote by t
∈ [0, C], the admission control
threshold; that is, the average number of resource units that
must remain free after accepting the new SU requests must
be t or higher. Clearly, these resources are reserved for SUs
performing SH. Then, increasing t causes a reduction of
the forced termination probability but, at the same time,
increases the blocking probability perceived by new SU
requests and vice versa. Note also that PUs are unaffected by
the admission policy, as SUs are transparent to them.
A PU arrival in state x will not force the termination of
SUs when the system state complies with C
− b(x) ≥ N,as
the execution of SH will allow SUs to continue their ongoing
session in a new unused channel, which are guaranteed to
exist given the condition above. On the other hand, when

C
−b(x) <N, x
1
<M, a PU arrival will preempt b(x+e
1
)−C
SUs. Let k(x) be the number of preemptions in state x, then
k
(
x
)
= min{r ∈ N | b
(
x + e
1
−re
2
)
≤ C}.
(11)
Note that k(x)
= 0 when C −b(x) >N, that is, it will be null
for a high portion of the state space.
As before, let r
xy
be the transition rate from x to y, x ∈ S,
then
r
xy
=

⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
a
1
(
x
)

λ
1
if y = x + e
1
−k
(
x
)
e
2
,
k
= 0, ,min
(
x
2
, N
)
,
a
2
(
x
)
λ
2
if y = x + e
2
,
x

i
μ
i
if y = x −e
i
,
0 otherwise.
(12)
The coefficients a
1
(x)anda
2
(x) denote the probabilities of
accepting a PU arrival and a SU arrival in state x,respectively.
It is clear that a
1
(x) = 1, if x + e
1
− k(x), e
2
∈ S,and0
otherwise. Given a policy setting t, a
2
(x) is determined as
follows:
a
2
(
x
)

=
⎧
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
1ifC − b
(
x + e
2
)
>
t,
1 −
(
t
−t
)
if C
−b
(
x + e
2
)

=t,
0 otherwise.
(13)
Figure 1 shows the state transition rates of the CTMC
that models the system dynamics. The stationary distri-
bution,
{π(x)}, is obtained by solving the global balance
equations (5) together with the normalization equation. The
blocking probability for SU requests, P
2
, the SUs forced
termination probability, P
ft
2
, and the SUs throughput, Th
2
,
are then computed using (7), (8)and(9), respectively.
Theanalyticalmodelsdescribedabovehavebeenvali-
dated through computer simulations. The simulation models
we designed mimic the behavior of the physical system, in
other words, the original system itself is simulated instead
of simulating just the CTMC. Thus, the validation offers a
guarantee on the correctness of the whole modeling process,
and not only about the generation and solution of the global
balance equations of the CTMC.
2.2. CAP Scheme to Limit the Interference Caused to PUs.
We assume that the SUs vacating rate induced by the arrival
of new PU sessions is a measure of the interference caused
by SUs to PUs, and we pursue to determine its value

when deploying the spectrum access and channel repacking
algorithms described in Section 1. Besides, we compare it to
the one obtained when deploying the conventional random
allocation scheme. A similar metric was used in [13]to
measure the interference.
When the system supports SH the channel allocation and
repacking algorithms have no impact on the performance
perceived by the SUs; that is, their blocking and forced
termination probabilities are not affected. Clearly, the finding
of free channels by arriving or vacated SUs depends only on
the number of ongoing PU and SU sessions and not on their
physical disposition on the spectrum.
It should be noted that repacking for PUs is not
considered. If the system deploys SH, CAP and repacking for
SUs, doing repacking for PUs would only affect the algorithm
followed to find a free channel upon the arrival of a SU,
but not to the system performance (P
ft
2
and interference). As
described above, P
ft
2
is not affected by the channel allocation
and repacking algorithms used. In the same system, a PU
arrival will experience interference when there are SUs
occupying the PU band with the lowest order available.
Clearly, this occurs when there are not enough free channels
to accommodate the newly arrived PU without some SUs
vacating the channel they are using (C

− b(x) <N) then a
previous repacking of PUs would have not helped.
EURASIP Journal on Wireless Communications and Networking 5
Table 2: Transition rates in system 3b with M = 2.
Current state Next state Transition rate
(i, j)(P, k)
k
= min( j + i,N) λ
1
(i, P)(P, P)
(P, j)(P, P)
(i, j), j<N (i, j +1)
(i, N), i<N (i +1,N)
(P, j), j<N (P, j +1) λ
2
(i, P), i<N (i +1,P)
(i, P)(i,0)
(P, j)(0,j) μ
1
(P, P)(0,P), (P,0) 2μ
1
(i, j)(i − 1, j),(i, j − 1) (i + j)μ
2
(i, P)(i − 1,P) iμ
2
(P, j)(P, j −1) jμ
2
2.2.1. System 3. System 3b (3a) is characterized by support-
ing (not supporting) SH, deploying the Complete Sharing
admission policy, deploying CAP and no repacking.

For the type of system under study, the state space of
its CTMC model grows very quickly with the number of
channels, as the state representation must describe not only
the number of ongoing PU and SU sessions, but also the
disposition of the allocated channels on the spectrum. More
specifically, the number of states is (N +2)
M
. This makes the
solution of the CTMC intractable for any practical scenario.
Instead, we developed a simulation model and validated
it with the analytical model of a simple scenario. This
scenario has M
= 2bandsforPUsandM × N subbands
or channels for SUs. The set of feasible states is
S :
=

y =

y
1
, y
2

: y
1
, y
2
∈{P,0, , N}


,
(14)
where y
1
(y
2
) describes the state of the N leftmost (right-
most) channels. When y
i
= 0 the band is empty, when y
i
= P
it is occupied by a PU, otherwise the number of SUs in the
band can be y
i
= 1, , N. The transition rates of the CTMC
that models system 3b are displayed in Ta ble 2.
Note that, for example, at state (1, P), where there is one
SU occupying one channel (out of N) in the first band of
N channels and one PU occupying the second band, the
actual channel allocated to the SU cannot be determined, but
this information is irrelevant for the performance parameters
of interest. When N
= 2, the system has 16 states,
independently of SH being supported or not.
As an example, for a system supporting SH and CAP,
the vacating rate γ
v
and the forced termination rate γ
ft

can be determined from (15)and(16). The first term in
(15) accounts for the contribution to the SUs vacation rate
of states with no PUs in the system. In these states, a
PU arrival will occupy the first band, vacating i SUs. The
second and third terms account for the contribution of the
states where there is a PU in the first or the second band,
respectively. Then, the arrival of a new PU would vacate j
or i SUs, respectively. The first term in (16) accounts for the
contribution to the SUs forced termination rate of states with
no PUs in the system. Note that if i SUs are found in the
first band, the arrival of a PU will force the termination of
one SU when there are N
− i + 1 SUs in the second band, of
two SUs when there are N
− i + 2 SUs in the second band,
and so on. The second and third terms clearly account for
the contribution of states where there is a PU in the first and
second band, respectively,
γ
v
= λ
1
⎡
⎣
N

i=0
N

j=0

iπ

i, j

+
N

j=0
jπ

P, j

+
N

i=0
iπ
(
i, P
)
⎤
⎦
, (15)
γ
ft
= λ
1
⎡
⎣
N


i=0
i

j=0
jπ

i, N −i + j

+
N

j=0
jπ

P, j

+
N

i=0
iπ
(
i, P
)
⎤
⎦
.
(16)
To compare the results of the analytical and simulation

models we selected three parameters: the blocking probabili-
ties of PUs and SUs, and the forced termination probability of
SUs. For both systems, with and without SH support, results
clearly indicate a close agreement between the analytical and
simulation models.
2.2.2. System 4. This system is characterized by support-
ing SH, deploying the Complete Sharing admission policy,
deploying CAP and repacking (CAP+RP).
Clearly, repacking can be triggered when either a PU or
a SU leaves the system. Using the notation defined in the
previous section for a system with M
= N = 2, repacking
would take place, for example, when a SU leaves from the
upper band and the system state changes from (1,2) to (1, 1).
Note that as N
= 2, a maximum of two SUs fit into the upper
band. At this point, it is more convenient to move the SU
in the lower band to the empty channel in the upper band,
avoiding in this way future interference if a PU arrives. Then,
repacking would make the system move from state (1, 1) to
state (0, 2) instantaneously.
As in the previous section, we evaluate the system by
simulation and validate the simulation model by a simple
analytical model. For M
= N = 2, the analytical model
has 12 states, clearly less states than in a system without
repacking, as now some states are not feasible, as shown in
the previous example.
To compare the results of the analytical and simulation
models we selected the same parameters of merit. Again,

these results indicate an excellent agreement between the
analytical and simulation models.
3. Effectiveness of the Proposed Mechanisms
In this section we evaluate the effectiveness of incorporating
the Fractional Guard Channel admission policy to limit the
P
ft
2
,aswellastheeffectiveness of incorporating CAP and
repacking to limit the interference caused to PUs.
Unless otherwise specified, the reference scenario for the
numericalevaluationisdefinedby:M
= 10, N = 8, C =
M × N = 80, μ
1
= 1andμ
2
= 1. In some scenarios,
we consider that the load offered by PUs is such that their
6 EURASIP Journal on Wireless Communications and Networking
blocking probability is P
1
= 0.01, which is achieved at λ
1
=
4.4612. Following common conventions, we do not specify
the unit of the rates although typical values are expressed in
s
−1
. For the simulation result 95% confidence intervals are

represented. The confidence intervals have been computed
using 15 different simulation runs initialized with different
seeds.
3.1. Effectiveness of AC to Limit the P
ft
2
. The throughput
achieved by SUs in systems 1 and 2 is shown in Figure 2,
where we depict both the results of the analytical and the
simulation models. Note the excellent agreement between the
analytical and simulation results. Note also that the diameter
of the confidence intervals are really small. This is the reason
why confidence intervals will not be shown in the rest of the
figures.
The authors of [9] suggest that a natural way of
configuring a DSA system of similar characteristics to ours
is to choose t for each SU arrival rate, such that the Th
2
is maximized. As observed in previous figures, it is not
possible to determine an optimum operating point beyond
the obvious one that is to deploy SH and t
= 0. We believe
that the role of reservation in DSA systems might be the
same as its classical role in cellular systems; that is, to limit
the forced termination probability of SUs. Note also that for
the reservation values deployed, Th
2
is always higher when
deploying SH and reservation than when not deploying SH.
Deploying SH reduces the forced termination rate, which

increases the successful completion rate.
One of the most interesting results of the study is the
evolution of P
ft
2
with the SUs arrival rate, which is shown
in Figure 3. Observe that it seems to have a counterintuitive
behavior. Intuitively, one would expect that P
ft
2
should
increase with the SUs arrival rate. However in a system
without SH it has the opposite behavior. Note also that
in a system with reservation, and particularly for some
reservation values like t
= 10 or higher, the forced
termination first decreases, attaining a minimum, and then
increases. The P
ft
2
depends on the ratio of forced terminations
to accepted sessions. By comparing the evolution of the
forced termination rate with the SUs acceptance rate for the
interval of arrival rates of interest (not shown here), these
phenomena can be easily explained.
As expected, the P
ft
2
can be controlled by adapting the
threshold t according to the system trafficload.

3.2. Effectiveness of CAP and Repacking to Limit γ
v
. To
evaluate the effectiveness of CAP and repacking we obtained
the evolution of the SUs vacating rate γ
v
with λ
1
in systems
2, 3a, and 4, when λ
2
= 20. We chose λ
2
= 20 as the
P
ft
2
is around 0.1 for a system with SH and λ
1
= 4.4612,
which we consider a practical value. Recall that system 2
(S2) deploys the conventional random channel allocation
algorithm, while systems 3a (S3a) and 4 (S4) deploy CAP
and CAP and repacking (CAP+RP), respectively. To highlight
the results of the study, we represent in Figure 4 what we
define as the interference reduction factor; that is, the ratios
γ
v
(S2)/γ
v

(S3a) and γ
v
(S2)/γ
v
(S4).
1
5
10
15 20
25
30
0
5
10
15
20
25
λ
2
Th
2
Simulation, no SH
Simulation, SH, t
= 0
Simulation, SH, t
= 5
Simulation, SH, t
= 10
Analytical, no SH
Analytical, SH, t

= 0
Analytical, SH, t
= 5
Analytical, SH, t
= 10
Figure 2: Throughput of SUs with the arrival rate of SUs when λ
1
=
4.4612.
0
5 1015202530
0
0.05
0.15
0.25
0.35
0.45
0.1
0.2
0.3
0.4
λ
2
No SH
SH, t
= 0
SH, t
= 5
SH, t
= 10

P
ft
2
Figure 3: Forced termination of SUs with the arrival rate of SUs,
when λ
1
= 4.4612.
Clearly, the proposed mechanisms are quite effective as
they reduce the vacating rate induced by the arrival of PUs by
approximately one order of magnitude or more for practical
operating values. Note also that, as expected, the interference
reduction factor is higher when repacking is used.
4. Adaptive Admission Control Scheme
In this section, we describe an adaptive admission control
scheme that is able to limit simultaneously both the forced
EURASIP Journal on Wireless Communications and Networking 7
12344.46 5 6
10
0
10
1
10
2
10
3
10
4
λ
1
γ

v
(S2)/γ
v
(S3a, S4)
S3a (CAP)
S4 (CAP + RP)
Figure 4: Interference reduction factor with the arrival rate of
primary users when λ
2
= 20.
termination probability of SUs and the interference caused
to PU communications by the operation of the SUs.
Our scheme generalizes a novel adaptive AC strategy
introduced in [18] and developed further in [19], which
operates in coordination with the well-known trunk reser-
vation policy named Multiple Guard Channel (MGC).
However, one of the novelties of the new proposal is that
now the adaptive scheme is able to control simultaneously
multiple objectives for the same arrival flow (SU arrivals), as
opposed to only one objective per flow in previous proposals.
The definition of the MGC policy is as follows. One
threshold parameter is associated with each objective. For
example, in a system with two objectives, one for the P
ft
2
and
another for the interference. Let t
ft
, t
if

∈ N be their associated
thresholds. Then, a SU arrival in state x is accepted if b(x +
e
2
) ≤ t, t = min{t
ft
, t
if
}, and blocked otherwise. Therefore,
t is the amount of resources that SUs have access to and
decreasing (increasing) it reduces (augments) the acceptance
rate of SU requests, which will in turn decrease (increase)
both P
ft
2
and the interference. Note that the definition of t in
this section and in Section 2 are different.
For the sake of clarity, the operation of our scheme is
described assuming that arrival processes are stationary and
the system is in steady state. We denote by B
ft
2
the objective for
the forced termination probability perceived by SUs (P
ft
2
). In
practice, we can assume without loss of generality that B
ft
2

can
be expressed as a fraction n
ft
/d
ft
, n
ft
, d
ft
∈ N. When P
ft
2
= B
ft
2
,
it is expected that, in average, n
ft
forced termination events
and (d
ft
−n
ft
) successfully completed SU session events, will
occur out of d
ft
accepted SU session events. For example, if
the objective is B
ft
2

= 1/100, then n
ft
= 1andd
ft
= 100. It
seems intuitive to think that the adaptive scheme should not
change t
ft
when the system is meeting its forced termination
probability objective and, on the contrary, adjust it on the
required direction when the perceived P
ft
2
is different from its
objective.
Given that the MGC policy uses integer values for the
threshold parameters, to limit P
ft
2
to its objective B
ft
2
= n
ft
/d
ft
,
we propose to perform a probabilistic adjustment in the
following way.
(i) At the arrival of a PU, if it forces the termination of m

SUs, do
{t
ft
← t
ft
−m} with probability 1/n
ft
.
(ii) When a SU session is accepted, do
{t
ft
← t
ft
+1} with
probability 1/d
ft
.
Intuitively, under stationary traffic conditions, if P
ft
2
= B
ft
2
then, on average, t
ft
will be increased by 1 and decreased by
1everyd
ft
accepted requests, that is, its mean value is kept
constant.

We define a new measure for the interference by consid-
ering the fraction of PU arrivals that vacate exactly n SUs,
n>0, and denote it by P
if
(n).Letusdenoteitsobjective
by B
if
(n) = n
if
n
/d
if
n
and the admission control threshold
associated to it by t
if
n
. Then, to limit P
if
(n)toitsobjective,we
propose to perform the following probabilistic adjustment at
the arrival of each PU.
(i) With probability 1/d
if
n
do {t
if
n
← t
if

n
+1}.
(ii) Additionally, if it vacates exactly n SUs, then with
probability 1/n
if
n
do {t
if
n
← t
if
n
−1}.
Again, under stationary traffic, if P
if
(n) = B
if
(n) then, on
average, t
if
n
is increased by 1 and decreased by 1 every d
if
n
offered PU requests, that is, its mean value is kept constant.
When the traffic is nonstationary, the adaptive scheme
will continuously adjust the thresholds in order to meet the
objectives if possible, adapting to any mix of traffic. Clearly,
in the operation of this simple scheme no assumptions
have been made concerning the arrival processes or the

distributions of the session duration.
An important consequence of the definition of the
interference probabilities
{P
if
(n)} is that now we have the
possibility to limit what we call the interference distribution.
That is, we can define one objective for each of the elements
of
{P
if
(n)}, n = 1, , N, or combinations of them, in order
to give less importance (allow higher probabilities) to events
thatcreatelowerinterference(smallvaluesofn)andmore
importance (allow smaller probabilities) to events that create
higher interference (high values of n).
Figure 5 describes the procedure followed at a SU arrival
to decide upon the acceptance or rejection of the new
request. If the system defines multiple objectives for the
interference and therefore manages multiple thresholds, then
t
if
would be the minimum of all these thresholds.
4.1. Numerical Results. Theadaptiveschemehasbeeneval-
uated in systems 5a and 5b by simulation. We used the
parameter values defined in Section 3.
As an example, let us consider P
if
(n ≤ N) =


N
n=1
P
if
(n);
that is, the fraction of PU arrivals that are interfered by SUs.
Figure 6 shows the variation of P
ft
2
and the interference with
the SUs arrival rate when the objectives are B
ft
2
≤ 0.05 and
B
if
(n ≤ N = 8) ≤ 0.1. As observed, the scheme is able to
limit P
ft
2
and P
if
(n ≤ N) to their objectives or below, and
8 EURASIP Journal on Wireless Communications and Networking
(1) D, D
ft
and D
if
are internal flags.
(2) Execute at every SU arrival:

(3) if x
1
N + x
2
<C: (free resources available)
(4) if b(x)+b
2
≤ t
ft
then D
ft
= 1
else D
ft
= 0
(5) if b(x)+b
2
≤ t
if
then D
if
= 1
else D
if
= 0
(6) D
= D
ft
&D
if

(7) if D = 1 then accept SU request
else reject SU request
(8) else reject SU request
Figure 5: Admission control scheme for SUs.
110 20 30
40
500
0.01
0.02
0.03
0.04
0.05
λ
2
Probability
P
ft
2
, S5b
P
ft
2
, S5a
P
if
(n ≤ N), S5b
P
if
(n ≤ N), S5a
Figure 6: P

ft
2
and interference (P
if
(n ≤ N)) with λ
2
in S5a and S5b.
the interference is lower when repacking is used. Note that
the limiting objective in both systems is B
ft
2
,asP
if
(n ≤ N)
remains below its objective. In other words, t
ft
is lower than
t
if
(n ≤ N) in both systems for the load range considered.
Note also that we have chosen a wide arrival rate range to
show the effectiveness of the adaptive scheme. However, if
the system does not reserve resources to accommodate SHs
then P
ft
2
> 0.05 even for small values of λ
2
.
Figure 7 shows the variation of the SUs throughput with

the SUs arrival rate. As a reference, we also plot the results
obtained for systems 3a and 4. Recall that systems 3a and
5a do not support SH, deploy CAP but no repacking, while
systems 4 and 5b do support SH, deploy CAP and repacking.
However, S5a and S5b deploy the adaptive AC scheme, while
S3a and S4 do not.
We consider that system loads that make P
ft
2
> 0.1areof
no practical interest. Although not shown, in systems 3a and
4, P
ft
2
> 0.1forλ
2
> 20. Then, restricting to the load range
of interest for S3a and S4, Th
2
is higher in S5a and S5b than
in S3a and S4. The improvement comes from the fact that
limiting P
ft
2
increases the rate of SUs that complete service
successfully. As λ
2
keeps on growing, the blocking of SU setup
01 10 20 30 40 50
0

5
10
15
20
25
30
35
40
λ
2
Th
2
S5b
S5a
S3a
S4
Figure 7: SUs throughput with λ
2
in S5a, S5b, S3a and S4.
01 10
20
30 40 50
0
0.01
0.02
0.03
0.04
0.05
0.06
Probability

λ
2
P
ft
2
, S5b
P
ft
2
, S5a
P
if
(n ≤ 3), S5b
P
if
(n ≤ 3), S5a
P
if
(n>3), S5b
P
if
(n>3), S5a
Figure 8: P
ft
2
and interference with λ
2
in S5a and S5b.
requests increases as the AC scheme must keep on limiting
P

ft
2
. This higher SUs blocking limits the SUs acceptance rate
and therefore the growth of Th
2
.
As another example, let us consider P
if
(n ≤ 3) and
P
if
(n>3); that is, the fraction of PU arrivals that perceive
low interference (n
≤ 3) and the fraction that perceive high
interference (n>3). Figure 8 plots P
ft
2
and the interference
as a function of the SUs arrival rate, when the objectives are
B
ft
2
≤ 0.05, B
if
(n ≤ 3) = 0.03 and B
if
(n>3) = 0.01. The
scheme is able to limit P
ft
2

, P
if
(n ≤ 3) and P
if
(n>3) to their
objectives or below. For λ
2
≤ 20 the limiting objective in S5a
and S5b is B
if
(n>3), as P
ft
2
and P
if
(n ≤ 3) are below their
EURASIP Journal on Wireless Communications and Networking 9
0.50.75 1 1.52 5 10
0
0.05
0.1
0.2
0.15
0.25
μ
2
P
ft
2
λ

2
= 10, CV(s
2
) = 1
λ
2
= 10,CV(s
2
) = 0.5
λ
2
= 10, CV(s
2
) = 2
λ
2
= 20,CV(s
2
) = 1
λ
2
= 20,CV(s
2
) = 0.5
λ
2
= 20,CV(s
2
) = 2
Figure 9: Sensitivity of P

ft
2
to E[s
2
] and CV[s
2
]insystem3a.
objectives. However, for λ
2
> 20 the limiting objective in S5a
is B
if
(n ≤ 3), while in S5b is still B
if
(n>3).
4.2. Adaptivity of the AC Scheme. As discussed above, the
adaptive scheme can operate with any arrival process and
distribution of the session duration. As an example, we
study in system 5a the adaptivity of the scheme to different
distributions of the SUs session duration random variable
(s
2
).
We consider three distributions: exponential (CV[s
2
] =
1), Erlang (CV[s
2
] < 1) and hyperexponential (CV[s
2

] >
1). Please refer to any textbook, for example [20], for
the definition of the probability density functions of these
distributions. For an Erlang-k distribution with E[s
2
] = 1/μ
2
,
the standard deviation and the coefficient of variation are:
σ
2
= 1/(μ
2
√
k) and CV[s
2
] = 1/
√
k.Wesetk = 4toobtain
CV[s
2
] = 1/2. We use a special type of a two stage hyper-
exponetial distribution that requires only two parameters
(mean and standard deviation) for characterization [21]. The
standarddeviationisselectedtoobtainCV[s
2
] = 2. Note that
in our results we also vary the mean (E[s
2
] = 1/μ

2
), then the
offered load (λ
2
/μ
2
) is maintained constant to make results
comparable.
To motivate the interest of deploying adaptive schemes,
Figure 9 shows the variation of P
ft
2
in system 3a. Note that
both the CV and the mean of s
2
have a great impact on P
ft
2
.
In fact, in Figure 9 we get one order of magnitude variation
in the values of P
ft
2
for a constant offered load.
The effectiveness of the adaptive scheme to cope with
traffic having different characteristics is clearly shown in Fig-
ures 10, 11 and 12. The forced termination and interference
objectives have been set to B
ft
2

≤ 0.05, B
if
(n ≤ 3) = 0.03 and
B
if
(n>3) = 0.01. As in other scenarios, the load of PUs is
adjusted such that their blocking probability is 0.01. Observe
0.75 1 1.52 5 10
0
0.01
0.02
0.03
0.04
0.05
0.06
μ
2
0.5
P
ft
2
λ
2
= 10,CV(s
2
) = 1
λ
2
= 10,CV(s
2

) = 0.5
λ
2
= 10,CV(s
2
) = 2
λ
2
= 20,CV(s
2
) = 1
λ
2
= 20,CV(s
2
) = 0.5
λ
2
= 20,CV(s
2
) = 2
Figure 10: P
ft
2
with μ
2
and CV[s
2
] in S5a.
0.50.75 1 1.52 5 10

0
0.005
0.015
0.025
0.035
0.01
0.02
0.03
μ
2
P
if
(n ≤ 3)
λ
2
= 10,CV(s
2
) = 1
λ
2
= 10,CV(s
2
) = 0.5
λ
2
= 10,CV(s
2
) = 2
λ
2

= 20,CV(s
2
) = 1
λ
2
= 10,CV(s
2
) = 0.5
λ
2
= 10,CV(s
2
) = 2
Figure 11: Interference (P
if
(n ≤ 3)) with μ
2
and CV[s
2
] in S5a.
that the proposed scheme is able to adapt and limit the forced
termination and the interference under all conditions.
In Figure 10, we observe that for μ
2
< 0.75 the limiting
objective is B
ft
2
, as the interference probabilities are below
their objectives. However, for μ

2
> 0.75 this behavior is
reversed. This is due to the fact that to meet one of the
interference objectives the rate of admitted SUs into the
system is reduced (the threshold is reduced), as observed
in Figures 11 and 12. Note that a similar phenomenon was
described in Figure 8.Clearly,forλ
2
= 10 and μ
2
∈ [1, 5] the
limiting objective is B
if
(n>3), while for μ
2
> 5 the limiting
objective is B
if
(n ≤ 3). For λ
2
= 20 and μ
2
> 1 the limiting
10 EURASIP Journal on Wireless Communications and Networking
0.50.75 1 1.52 5
10
0
0.001
0.01
0.0025

0.0075
0.0125
0.005
μ
2
P
if
(n>3)
λ
2
= 10, CV(s
2
) = 1
λ
2
= 10, CV(s
2
) = 0.5
λ
2
= 10, CV(s
2
) = 2
λ
2
= 20,CV(s
2
) = 1
λ
2

= 20,CV(s
2
) = 0.5
λ
2
= 20,CV(s
2
) = 2
Figure 12: Interference (P
if
(n>3)) with μ
2
and CV[s
2
] in S5a.
objective is B
if
(n ≤ 3), that is, the fraction of PU arrivals
experiencing low interference (P
if
(n ≤ 3)) is at its objective
or close, while the fraction experiencing high interference
(P
if
(n>3))isconsiderablybelowitsobjective.
Finally, if we compare Figures 9 and 10 we conclude that
the operation of the adaptive scheme makes P
ft
2
insensitive

to the distribution of the SUs service time, which is an
additional robustness advantage. A similar conclusion can be
obtained for P
if
(n ≤ 3) and partially for P
if
(n>3).
5. Conclusions
We studied the effectiveness of the Fractional Guard Channel
admission policy to guarantee the QoS perceived by SUs,
defined in terms of their forced termination probability.
We modeled the system as a CTMC which was validated
by computer simulation. Results showed that, contrary to
what has been proposed, the throughput of SUs cannot be
maximized by configuring the reservation parameter. We
also showed that the probability of forced termination can
be limited by setting appropriately the reservation threshold.
We also studied the QoS perceived by PUs, defined in
terms of the interference caused to PU communications by
the operation of SUs. We proposed and evaluated different
mechanisms to reduce the interference based on simple
spectrum access and channel repacking algorithms. In this
case, to cope with the state explosion as the number of system
channels grows, we resorted to simulation models that were
validated by developing analytical models for systems of
manageable size. We compared the interference in a system
that uses the proposed mechanisms with the interference
in a system that uses the common random access scheme.
Numerical results showed that the interference reduction can
be of one order of magnitude or higher when using the new

mechanisms with respect to the random access case.
Finally, we proposed and evaluated a novel adaptive
admissioncontrolschemeforSUsthatisabletolimit
simultaneously the probability of forced termination of SUs
and the interference. The operation of our scheme is based
on simple balance equations which hold for any arrival
process and holding time distribution. Our proposal has two
relevant features, its ability to guarantee a certain degree of
QoSforPUsandSUsunderanytraffic characteristics, and its
implementation simplicity.
Acknowledgments
This work has been supported by the Spanish Ministry of
Science and Innovation and the European Commission (30%
PGE, 70% FEDER) under Projects TSI2007-66869-C02-02
and TIN2008-06739-C04-02.
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